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L1-Norm Quantile Regression Screening Rule via the Dual Circumscribed Sphere

L1-norm quantile regression is a common choice if there exists outlier or heavy-tailed error in high-dimensional data sets. However, it is computationally expensive to solve this problem when the feature size of data is ultra high. As far as we know, existing screening rules can not speed up the computation of the ℓ1 -norm quantile regression, which dues to the non-differentiability of the quantile function/pinball loss. In this paper, we introduce the dual circumscribed sphere technique and propose a novel ℓ1 -norm quantile regression screening rule. Our rule is expressed as the closed-form function of given data and eliminates inactive features with a low computational cost. Numerical experiments on some simulation and real data sets show that this screening rule can be used to eliminate almost all inactive features. Moreover, this rule can help to reduce up to 23 times of computational time, compared with the computation without our screening rule.

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Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE; Vol.44 No.10 Part 1 October 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 6254-6263

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

COMPUTATIONAL EFFICIENCY
SCREENING RULE
L1 -NORM QUANTILE REGRESSION
DUAL CIRCUMSCRIBED SPHERE

Info Detail Spesifik

DOI: 10.1109/TPAMI.2021.3087160

Pernyataan Tanggungjawab

Pan Shang, Lingchen Kong

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